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Modern industrial processes are highly instrumented with more frequent recording of data. This provides abundant data for safety analysis; however, these data resources have not been well used. This paper presents an integrated dynamic failure prediction analysis approach using principal component analysis (PCA) and the Bayesian network (BN). The key process variables that contribute the most to process performance variations are detected with PCA, while the Bayesian network is adopted to model the interactions among these variables to detect faults and predict the time-dependent probability of system failure. The proposed integrated approach uses big data analysis. The structure of BN is learned using past historical data. The developed BN is used to detect faults and estimate system failure risk. The risk is updated subsequently as new process information is collected. The updated risk is used as a decision-making parameter. The proposed approach is validated through a case of a crude oil distillation unit operation.

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